Teaching materials
\(~\)
‘hungry forager’ primer:
‘satiated forager’ primer:
Pigeon footage is available on the github page associated with this
report. It can be accessed here: https://github.com/tomkeaney/Biased_pigeons by
downloading the pigeon_video.mp4 file.
\(~\)
Load packages and the data
library(tidyverse) # tidy style coding
library(brms) # Bayesian models
library(loo) # for information criteria
library(tidybayes) # Bayesian aesthetics
library(MetBrewer) # colours
library(kableExtra) # tables
library(patchwork) # putting plots together
library(DT) # for search- and saveable tables
library(pander) # for simpler tables
library(png) # to load images
library(grid) # to plot images
library(ggdag) # to draw dags
data <- read_csv("data/pigeon_data.csv") %>%
mutate(Student_ID = as.factor(Student_ID),
Year = as.factor(Year),
Foraging_prop = (Foraging_percentage / 100)) %>%
filter(Year %in% c("2021", "2022", "2023"),
Foraging_percentage != "NA",
Primer_understood != "NA") %>%
filter(Student_ID != "60" & Student_ID != "68" & Student_ID != "70" & Student_ID != "72") %>% # remove students that completed the task multiple times
#Student_ID %in% c("60", "68", "70", "72")) %>%
select(-c(First_name, Surname, Peck_mean)) %>% # remove names when ready
left_join(
read_csv("data/gender_data.csv") %>%
mutate(Student_ID = as.factor(Student_ID))
) %>%
rename(Observer_ID = Student_ID) %>%
select(Observer_ID, Year, Gender, everything())
data_peck <-
data %>%
filter(Peck_rate_2 != "NA") %>%
pivot_longer(cols = Peck_rate_1:Peck_rate_2, names_to = "Trial",
values_to = "Peck_rate")
# Create a function to build HTML searchable tables
my_data_table <- function(df){
datatable(
df, rownames=FALSE,
autoHideNavigation = TRUE,
extensions = c("Scroller", "Buttons"),
options = list(
dom = 'Bfrtip',
deferRender=TRUE,
scrollX=TRUE, scrollY=400,
scrollCollapse=TRUE,
buttons =
list('pageLength', 'colvis', 'csv', list(
extend = 'pdf',
pageSize = 'A4',
orientation = 'landscape',
filename = 'full_dataset')),
pageLength = 78
)
)
}
my_data_table(data)
Column explanations
Observer_ID: unique, anonymised identifier for each
observer.
Year: year that the experiment was conducted.
Gender: upon enrollment at The University of
Melbourne, students are asked to indicate their title. We identified
women as observers that answered “Miss” or “Ms” and Men as those who
that answered “Mr”. Those with entirely missing entries were coded as
“NA”.
Bias_treatment: the primer the observer received,
where ‘satiated’ indicates that the observers were provided information
prior to a trial that suggested pigeons were fell fed, whereas ‘hungry’
indicated that the pigeons were in poor condition and hungry.
Expectation: we asked the observers to indicate
whether they thought the pigeons would be hungry or satiated. We
included this question to test whether observers were appropriately
primed by their bias treatment.
Primer_understood: did the observer’s expectation
match the primer they received?
Foraging_percentage: the percentage of pigeons that
observers estimated to be foraging over a 15 second period, while
observing a large flock.
Peck_rate_1: the number of times a single chosen
pigeon pecked the ground over a 15 second period.
Peck_rate_2: the number of times a second chosen
pigeon pecked the ground over a 15 second period.
Foraging_prop: proportion of pigeons estimated to be
foraging
\(~\)
Question 1: is estimation of foraging percentage affected by bias
manipulations?
\(~\)
Exploring allocation versus expectation
1
\(~\)
We find that 22 of the 78 observers indicated a
feeding motivation expectation opposite to that implied by the primer
they were allocated.
This suggests that Expectation may be a better predictor
of foraging estimation than allocated Bias_treatment. The
relationship between these and all other variables that we expect to
play a role in this system are depicted in Figure 1.
We explicitly assess the effect of treatment and expectation by
fitting two models:
a model with allocated primer (Bias_treatment) as
the predictor variable
a model with indicated hunger expectation
(Expectation) as the predictor variable
\(~\)
\(~\)
Model 1: allocated primer
# First let's model the effect of bias treatment on foraging estimation
foraging_model_treatment <- brm(Foraging_prop ~ 0 + Bias_treatment,
data = data, family = Beta,
prior = c(prior(normal(0, 1.5), class = b),
prior(exponential(1), class = phi)),
iter = 6000, warmup = 2000, chains = 4, cores = 4,
control = list(adapt_delta = 0.8, max_treedepth = 10),
seed = 1, file = "fits/foraging_model_treatment")
foraging_model_treatment <- add_criterion(foraging_model_treatment, criterion = "loo", file = "fits/foraging_model_treatment")
foraging_model_treatment
## Family: beta
## Links: mu = logit; phi = identity
## Formula: Foraging_prop ~ 0 + Bias_treatment
## Data: data (Number of observations: 78)
## Draws: 4 chains, each with iter = 6000; warmup = 2000; thin = 1;
## total post-warmup draws = 16000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Bias_treatmentHungry -0.47 0.14 -0.74 -0.20 1.00 14927
## Bias_treatmentSatiated -0.47 0.15 -0.76 -0.17 1.00 14749
## Tail_ESS
## Bias_treatmentHungry 12186
## Bias_treatmentSatiated 11819
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 4.21 0.61 3.11 5.48 1.00 13695 12202
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
foraging_model_treatment_gender <- brm(Foraging_prop ~ 0 + Gender * Bias_treatment,
data = data, family = Beta,
prior = c(prior(normal(0, 1.5), class = b),
prior(exponential(1), class = phi)),
iter = 6000, warmup = 2000, chains = 4, cores = 4,
control = list(adapt_delta = 0.8, max_treedepth = 10),
seed = 1, file = "fits/foraging_model_treatment_gender")
Table S1. Posterior estimates of the percentage of
pigeon feeding rate, split by the primer observers were allocated.
new_data <- tibble(Bias_treatment = c("Hungry", "Satiated"))
new_data %>%
left_join(data %>% group_by(Bias_treatment) %>% summarise(`n observers` = n())) %>%
cbind(fitted(foraging_model_treatment, newdata = new_data, summary = T) %>%
as_tibble() %>%
mutate(across(1:4, ~ .x *100),
across(1:4, round, 2))) %>%
rename("Estimated proportion foraging" = Estimate,
"Bias treatment" = Bias_treatment) %>%
pander(split.cell = 20, split.table = Inf)
| Hungry |
42 |
38.47 |
3.25 |
32.24 |
44.99 |
| Satiated |
36 |
38.59 |
3.51 |
31.85 |
45.65 |
Model 2: indicated expectation
# fit the same model, except using participant expectation rather than allocated bias treatment
foraging_model_expectation <- brm(Foraging_prop ~ 0 + Expectation,
data = data, family = Beta,
prior = c(prior(normal(0, 1.5), class = b),
prior(exponential(1), class = phi)),
iter = 6000, warmup = 2000, chains = 4, cores = 4,
control = list(adapt_delta = 0.8, max_treedepth = 10),
seed = 1, file = "fits/foraging_model_expectation")
foraging_model_expectation <- add_criterion(foraging_model_expectation, criterion = "loo", file = "fits/foraging_model_expectation")
foraging_model_expectation
## Family: beta
## Links: mu = logit; phi = identity
## Formula: Foraging_prop ~ 0 + Expectation
## Data: data (Number of observations: 78)
## Draws: 4 chains, each with iter = 6000; warmup = 2000; thin = 1;
## total post-warmup draws = 16000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ExpectationHungry -0.40 0.13 -0.66 -0.14 1.00 15078 12139
## ExpectationSatiated -0.56 0.15 -0.87 -0.26 1.00 15944 12233
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 4.25 0.62 3.12 5.55 1.00 16348 12206
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
foraging_model_expectation_gender <- brm(Foraging_prop ~ 0 + Gender * Expectation,
data = data, family = Beta,
prior = c(prior(normal(0, 1.5), class = b),
prior(exponential(1), class = phi)),
iter = 6000, warmup = 2000, chains = 4, cores = 4,
control = list(adapt_delta = 0.8, max_treedepth = 10),
seed = 1, file = "fits/foraging_model_expectation_gender")
Table S2. Posterior estimates of the percentage of
pigeons foraging, split by the actual expectation of observers.
new_data_2 <- tibble(Expectation = c("Hungry", "Satiated"))
new_data_2 %>%
left_join(data %>% group_by(Expectation) %>% summarise(`n observers` = n())) %>%
cbind(fitted(foraging_model_expectation, newdata = new_data_2, summary = T) %>%
as_tibble() %>%
mutate(across(1:4, ~ .x *100),
across(1:4, round, 2))) %>%
rename("Estimated proportion foraging" = Estimate,
"Indicated expectation" = Expectation) %>%
pander(split.cell = 20, split.table = Inf)
| Hungry |
44 |
40.2 |
3.19 |
34.08 |
46.62 |
| Satiated |
34 |
36.38 |
3.56 |
29.61 |
43.56 |
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Build Figure 1a-d
\(~\)
Get posterior means and difference contrasts
# treatment model
draws_treatment <-
as_draws_df(foraging_model_treatment) %>%
mutate(Hungry = inv_logit_scaled(b_Bias_treatmentHungry) *100,
Satiated = inv_logit_scaled(b_Bias_treatmentSatiated)*100,
diff_contrast = (Hungry - Satiated)) %>%
select(Hungry, Satiated, diff_contrast) %>%
pivot_longer(names_to = "Treatment", values_to = "Posterior_estimate", cols = 1:3) %>%
mutate(Predictor = "Allocated primer")
p2 <-
draws_treatment %>%
filter(Treatment != "diff_contrast") %>%
ggplot(aes(x = Treatment, y = Posterior_estimate)) +
stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
point_interval = "median_qi", point_fill = "white",
shape = 21, point_size = 4, stroke = 1.5) +
scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
coord_flip(ylim = c(25, 55)) +
#geom_vline(xintercept = 0, linetype = 2) +
#scale_y_continuous(breaks = c(, 0, 1)) +
xlab("Allocated primer") +
ylab("Estimated % pigeons foraging") +
theme_bw() +
theme(legend.position = "none",
panel.grid.minor = element_blank(),
text = element_text(size = 14))
p3 <-
draws_treatment %>%
filter(Treatment == "diff_contrast") %>%
ggplot(aes(y = Posterior_estimate)) +
stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
point_interval = "median_qi", point_fill = "white",
shape = 21, point_size = 4, stroke = 1.5, scale =0.5) +
scale_fill_manual(values = met.brewer("Hiroshige")[4]) +
coord_flip(ylim = c(-20, 20)) +
geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
#scale_y_continuous(breaks = c(, 0, 1)) +
xlab(NULL) +
ylab("Hungry - Satiated difference\ncontrast (% points)") +
theme_bw() +
theme(legend.position = "none",
panel.grid.minor = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
text = element_text(size = 14))
# expectation model
draws_expectation <-
as_draws_df(foraging_model_expectation) %>%
mutate(Hungry = inv_logit_scaled(b_ExpectationHungry) *100,
Satiated = inv_logit_scaled(b_ExpectationSatiated)*100,
diff_contrast = (Hungry - Satiated)) %>%
select(Hungry, Satiated, diff_contrast) %>%
pivot_longer(names_to = "Treatment", values_to = "Posterior_estimate", cols = 1:3) %>%
mutate(Predictor = "Indicated expectation")
p4 <-
draws_expectation %>%
filter(Treatment != "diff_contrast") %>%
ggplot(aes(x = Treatment, y = Posterior_estimate)) +
stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
point_interval = "median_qi", point_fill = "white",
shape = 21, point_size = 4, stroke = 1.5) +
scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
coord_flip(ylim = c(25, 55)) +
#geom_vline(xintercept = 0, linetype = 2) +
#scale_y_continuous(breaks = c(, 0, 1)) +
xlab("Indicated expectation") +
ylab("Estimated % pigeons foraging") +
theme_bw() +
theme(legend.position = "none",
panel.grid.minor = element_blank(),
text = element_text(size = 14))
p5 <-
draws_expectation %>%
filter(Treatment == "diff_contrast") %>%
ggplot(aes(y = Posterior_estimate)) +
stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
point_interval = "median_qi", point_fill = "white",
shape = 21, point_size = 4, stroke = 1.5, scale =0.5) +
scale_fill_manual(values = met.brewer("Hiroshige")[4]) +
coord_flip(ylim = c(-20, 20)) +
geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
#scale_y_continuous(breaks = c(, 0, 1)) +
xlab(NULL) +
ylab("Hungry - Satiated difference\ncontrast (% points)") +
theme_bw() +
theme(legend.position = "none",
panel.grid.minor = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
text = element_text(size = 14))
\(~\)
Question 2: is estimation of feeding rate affected by bias
manipulations?
\(~\)
We asked observers to count the number of pecks of the ground that a
selected pigeon made over a 1 minute period. We use the number of pecks
that occur as a measure of feeding rate.
We first estimated a baseline peck rate by observing 35 pigeons. To
select the pigeons we observed, we split a still image of the foraging
video (taken at time zero) into a 43 x 21 cell grid. From the 122 cells
that contained pigeons, 40 were chosen by random number generation (see
code chunk below). In the event that multiple pigeons were present in
the cell, we selected the most prominent to observe. Five observations
were discarded - three due to overlap of the same pigeon between cells
that were selected by the random number generator, and two more as the
pigeons left the field of view during the video and could no longer be
tracked.
# curly brackets run all lines included within them
{set.seed(1) # so that sample produces a reproducible sequence
sample(1:122, 40, replace = FALSE)}
## [1] 121 68 39 1 34 87 43 14 82 59 51 97 85 21 106 54 74 7 73
## [20] 79 110 37 89 101 118 100 44 103 33 84 35 70 108 42 38 20 28 117
## [39] 96 91
The selected pigeons for baseline observation are shown in the image
below
img <- readPNG("pigeon_selection.png")
grid.raster(img)

\(~\)
Estimating baseline feeding rate
\(~\)
Load in the data
baseline_data <- read_csv("data/baseline_peck_data.csv") %>%
select(1:5) %>% # remove the comments column
pivot_longer(cols = 4:5, names_to = "Observation", values_to = "Peck_rate") %>%
mutate(ID = as.factor(ID),
Observation = str_remove(Observation, "Peck_count_")) %>%
rename(Pigeon_ID = ID) %>%
filter(!is.na(Peck_rate))
my_data_table <- function(df){
datatable(
df, rownames=FALSE,
autoHideNavigation = TRUE,
extensions = c("Scroller", "Buttons"),
options = list(
dom = 'Bfrtip',
deferRender=TRUE,
scrollX=TRUE, scrollY=400,
scrollCollapse=TRUE,
buttons =
list('pageLength', 'colvis', 'csv', list(
extend = 'pdf',
pageSize = 'A4',
orientation = 'landscape',
filename = 'baseline_dataset')),
pageLength = 78
)
)
}
my_data_table(baseline_data)
X and Y represent grid
coordinates.
Pigeon_ID identifies a specific pigeon
Observation indicates whether this was the first or
second scoring for a single pigeon. We scored each pigeon twice as
distant pigeons were difficult to observe and to ensure that the correct
pigeon was tracked throughout the minute of observation.
Peck_rate is the number of times the ground was
pecked over a minute of observation.
\(~\)
Fit a simple model to estimate median peck rate
baseline_peck_model_zi <-
brm(Peck_rate ~ 1 + (1|Pigeon_ID),
family = zero_inflated_negbinomial(), data = baseline_data,
prior = c( prior(normal(0, 1.5), class = Intercept),
prior(exponential(1), class = sd),
prior(exponential(1), class = shape),
prior(exponential(1), class = zi)),
chains = 4, cores = 4, warmup = 2000, iter = 6000,
file = "fits/baseline_peck_model")
# wrangle the output
baseline_peck_predictions <-
baseline_peck_model_zi %>%
as_draws_df() %>%
mutate(Baseline_estimate = exp(b_Intercept),
peck_rate_sd = exp(sd_Pigeon_ID__Intercept)) %>%
select(Baseline_estimate, peck_rate_sd)
baseline_data %>%
distinct(Pigeon_ID) %>%
summarise(`n pigeons observed` = length(Pigeon_ID)) %>%
bind_cols(
fitted(baseline_peck_model_zi, summary = T, re_formula = NA) %>%
as_tibble() %>%
distinct(Estimate, .keep_all = T) %>%
mutate(across(1:4, round, 2)) %>%
rename(`Baseline median peck rate / per min` = Estimate)) %>%
pander()
\(~\)
Experimental data
\(~\)
Exploring allocation versus expectation
2.0
\(~\)
Once again, we expect that attention paid to and/or comprehension of
the primer statement has a large effect on observers’ perception of
pigeon foraging.
Lets again fit our two models:
a model with allocated primer (Bias_treatment) as
the predictor variable
a model with indicated hunger expectation
(Expectation) as the predictor variable
\(~\)
Model 1: allocated primer
# First let's model the effect of bias treatment on peck rate
peck_model_treatment <-
brm(Peck_rate ~ 0 + Bias_treatment + (1|Observer_ID),
data = data_peck, family = negbinomial,
prior = c(prior(normal(0, 1.5), class = b),
prior(exponential(1), class = sd)),
iter = 6000, warmup = 2000, chains = 4, cores = 4,
control = list(adapt_delta = 0.9, max_treedepth = 12),
seed = 1, file = "fits/peck_model_treatment")
peck_model_treatment <- add_criterion(peck_model_treatment, criterion = "loo", file = "fits/peck_model_treatment")
peck_model_treatment
## Family: negbinomial
## Links: mu = log; shape = identity
## Formula: Peck_rate ~ 0 + Bias_treatment + (1 | Observer_ID)
## Data: data_peck (Number of observations: 156)
## Draws: 4 chains, each with iter = 6000; warmup = 2000; thin = 1;
## total post-warmup draws = 16000
##
## Group-Level Effects:
## ~Observer_ID (Number of levels: 78)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.21 0.15 0.01 0.54 1.00 4493 7497
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Bias_treatmentHungry 2.32 0.13 2.06 2.57 1.00 14993
## Bias_treatmentSatiated 2.16 0.14 1.89 2.44 1.00 19527
## Tail_ESS
## Bias_treatmentHungry 10405
## Bias_treatmentSatiated 11645
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 0.91 0.13 0.69 1.18 1.00 13608 8978
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
peck_model_treatment_gender <-
brm(Peck_rate ~ 0 + Gender * Bias_treatment + (1|Observer_ID),
data = data_peck, family = negbinomial,
prior = c(prior(normal(0, 1.5), class = b),
prior(exponential(1), class = sd)),
iter = 6000, warmup = 2000, chains = 4, cores = 4,
control = list(adapt_delta = 0.9, max_treedepth = 12),
seed = 1, file = "fits/peck_model_treatment_gender")
Table S3. The estimated peck rate of foraging
pigeons, split by the primer observers were allocated.
new_data %>%
bind_cols(fitted(peck_model_treatment, newdata = new_data, summary = T, re_formula = NA) %>%
as_tibble() %>%
mutate(across(1:4, round, 2))) %>%
left_join(data_peck %>% group_by(Bias_treatment) %>%
distinct(Observer_ID) %>% summarise(`n pigeons observed` = n())) %>%
rename("Estimated peck rate" = Estimate,
"Bias treatment" = Bias_treatment) %>%
bind_rows(fitted(baseline_peck_model_zi, summary = T, re_formula = NA) %>%
as_tibble() %>%
distinct(Estimate, .keep_all = T) %>%
mutate(across(1:4, round, 2)) %>%
rename("Estimated peck rate" = Estimate) %>%
mutate(`Bias treatment` = "Baseline") %>%
bind_cols(baseline_data %>%
distinct(Pigeon_ID) %>%
summarise(`n pigeons observed` = length(Pigeon_ID)))) %>%
select(`Bias treatment`, `n pigeons observed`, everything()) %>%
pander(split.cell = 20, split.table = Inf)
| Hungry |
42 |
10.23 |
1.34 |
7.83 |
13.12 |
| Satiated |
36 |
8.74 |
1.24 |
6.59 |
11.44 |
| Baseline |
35 |
1.32 |
0.51 |
0.54 |
2.53 |
Model 2: Indicated expectation
# fit the same model, except using participant expectation rather than allocated bias treatment
peck_model_expectation <- brm(Peck_rate ~ 0 + Expectation + (1|Observer_ID),
data = data_peck, family = negbinomial,
prior = c(prior(normal(0, 1.5), class = b),
prior(exponential(1), class = sd)),
iter = 6000, warmup = 2000, chains = 4, cores = 4,
control = list(adapt_delta = 0.95, max_treedepth = 12),
seed = 1, file = "fits/peck_model_expectation")
peck_model_expectation <- add_criterion(peck_model_expectation, criterion = "loo", file = "fits/peck_model_expectation")
peck_model_expectation
## Family: negbinomial
## Links: mu = log; shape = identity
## Formula: Peck_rate ~ 0 + Expectation + (1 | Observer_ID)
## Data: data_peck (Number of observations: 156)
## Draws: 4 chains, each with iter = 6000; warmup = 2000; thin = 1;
## total post-warmup draws = 16000
##
## Group-Level Effects:
## ~Observer_ID (Number of levels: 78)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.18 0.13 0.01 0.48 1.00 4710 7228
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ExpectationHungry 2.41 0.12 2.18 2.66 1.00 16705 11820
## ExpectationSatiated 2.00 0.14 1.73 2.28 1.00 18644 12176
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## shape 0.93 0.12 0.71 1.20 1.00 14884 11083
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
#loo_compare(peck_model_treatment, peck_model_expectation)
peck_model_expectation_gender <- brm(Peck_rate ~ 0 + Gender * Expectation + (1|Observer_ID),
data = data_peck, family = negbinomial,
prior = c(prior(normal(0, 1.5), class = b),
prior(exponential(1), class = sd)),
iter = 6000, warmup = 2000, chains = 4, cores = 4,
control = list(adapt_delta = 0.95, max_treedepth = 12),
seed = 1, file = "fits/peck_model_expectation_gender")
Table S4. The estimated peck rate of foraging
pigeons, split by the indicated expectation of the observers.
new_data_2 %>%
bind_cols(fitted(peck_model_expectation, newdata = new_data_2, summary = T, re_formula = NA) %>%
as_tibble() %>%
mutate(across(1:4, round, 2))) %>%
left_join(data_peck %>% group_by(Expectation) %>%
distinct(Observer_ID) %>% summarise(`n pigeons observed` = n())) %>%
rename("Estimated peck rate" = Estimate,
"Indicated expectation" = Expectation) %>%
bind_rows(fitted(baseline_peck_model_zi, summary = T, re_formula = NA) %>%
as_tibble() %>%
distinct(Estimate, .keep_all = T) %>%
mutate(across(1:4, round, 2)) %>%
rename("Estimated peck rate" = Estimate) %>%
mutate(`Indicated expectation` = "Baseline") %>%
bind_cols(baseline_data %>%
distinct(Pigeon_ID) %>%
summarise(`n pigeons observed` = length(Pigeon_ID)))) %>%
select(`Indicated expectation`, `n pigeons observed`, everything()) %>%
pander(split.cell = 20, split.table = Inf)
| Hungry |
44 |
11.27 |
1.4 |
8.81 |
14.29 |
| Satiated |
34 |
7.46 |
1.06 |
5.62 |
9.81 |
| Baseline |
35 |
1.32 |
0.51 |
0.54 |
2.53 |
\(~\)
Build Figure 1e-h
\(~\)
Get posterior means and difference contrasts
# treatment model
peck_draws_treatment <-
as_draws_df(peck_model_treatment) %>%
mutate(Hungry = exp(b_Bias_treatmentHungry),
Satiated = exp(b_Bias_treatmentSatiated),
diff_contrast = (Hungry - Satiated)) %>%
select(Hungry, Satiated, diff_contrast) %>%
bind_cols(baseline_peck_predictions %>% select(Baseline_estimate)) %>%
pivot_longer(names_to = "Treatment", values_to = "Posterior_estimate", cols = 1:3) %>%
mutate(Predictor = "Allocated primer")
p6 <-
peck_draws_treatment %>%
filter(Treatment != "diff_contrast") %>%
ggplot(aes(x = Treatment, y = Posterior_estimate)) +
stat_slab(aes(y = Baseline_estimate),
linetype = 2, linewidth = 0.8, slab_fill = "white",
colour = "black") +
stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
point_interval = "median_qi", point_fill = "white",
shape = 21, point_size = 4, stroke = 1.5) +
scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
coord_flip(ylim = c(0, 20)) +
#geom_vline(xintercept = 0, linetype = 2) +
#scale_y_continuous(breaks = c(, 0, 1)) +
xlab("Allocated primer") +
ylab("Estimated pecks per min") +
theme_bw() +
theme(legend.position = "none",
panel.grid.minor = element_blank(),
text = element_text(size = 14))
p7 <-
peck_draws_treatment %>%
filter(Treatment == "diff_contrast") %>%
ggplot(aes(y = Posterior_estimate)) +
stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
point_interval = "median_qi", point_fill = "white",
shape = 21, point_size = 4, stroke = 1.5, scale =0.5) +
scale_fill_manual(values = met.brewer("Hiroshige")[4]) +
coord_flip(ylim = c(-5, 12)) +
geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
#scale_y_continuous(breaks = c(, 0, 1)) +
xlab(NULL) +
ylab("Hungry - Satiated difference\ncontrast (pecks per min)") +
theme_bw() +
theme(legend.position = "none",
panel.grid.minor = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
text = element_text(size = 14))
# expectation model
peck_draws_expectation <-
as_draws_df(peck_model_expectation) %>%
mutate(Hungry = exp(b_ExpectationHungry),
Satiated = exp(b_ExpectationSatiated),
diff_contrast = (Hungry - Satiated)) %>%
select(Hungry, Satiated, diff_contrast) %>%
bind_cols(baseline_peck_predictions %>% select(Baseline_estimate)) %>%
pivot_longer(names_to = "Treatment", values_to = "Posterior_estimate", cols = 1:3) %>%
mutate(Predictor = "Allocated primer")
p8 <-
peck_draws_expectation %>%
filter(Treatment != "diff_contrast") %>%
ggplot(aes(x = Treatment, y = Posterior_estimate)) +
stat_slab(aes(y = Baseline_estimate),
linetype = 2, linewidth = 0.8, slab_fill = "white",
colour = "black") +
stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
point_interval = "median_qi", point_fill = "white",
shape = 21, point_size = 4, stroke = 1.5) +
scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
coord_flip(ylim = c(0, 20)) +
#geom_vline(xintercept = 0, linetype = 2) +
#scale_y_continuous(breaks = c(, 0, 1)) +
xlab("Indicated expectation") +
ylab("Estimated pecks per min") +
theme_bw() +
theme(legend.position = "none",
panel.grid.minor = element_blank(),
text = element_text(size = 14))
p9 <-
peck_draws_expectation %>%
filter(Treatment == "diff_contrast") %>%
ggplot(aes(y = Posterior_estimate)) +
stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
point_interval = "median_qi", point_fill = "white",
shape = 21, point_size = 4, stroke = 1.5, scale =0.5) +
scale_fill_manual(values = met.brewer("Hiroshige")[4]) +
coord_flip(ylim = c(-5, 12)) +
geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
#scale_y_continuous(breaks = c(, 0, 1)) +
xlab(NULL) +
ylab("Hungry - Satiated difference\ncontrast (pecks per min)") +
theme_bw() +
theme(legend.position = "none",
panel.grid.minor = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
text = element_text(size = 14))
\(~\)
Find the difference between baseline and observer estimates
Table S5. The degree to which each group of
observer’s overestimates feeding rate (number of ground pecks per
minute)
baseline_peck_predictions %>% select(Baseline_estimate) %>% bind_cols(
as_draws_df(peck_model_treatment) %>%
mutate(Hungry = exp(b_Bias_treatmentHungry),
Satiated = exp(b_Bias_treatmentSatiated)) %>%
select(Hungry, Satiated)) %>%
mutate(`Bias treatment Satiated / Baseline` = Satiated / Baseline_estimate,
`Bias treatment Hungry / Baseline` = Hungry / Baseline_estimate) %>%
select(contains("Bias")) %>%
pivot_longer(cols = everything(), values_to = "estimate", names_to = "Stat") %>%
group_by(Stat) %>%
summarise_draws("median", "sd", ~quantile(.x, probs = c(0.025, 0.975), na.rm = TRUE), .cores = 4) %>%
select(-variable) %>%
bind_rows(
baseline_peck_predictions %>% select(Baseline_estimate) %>% bind_cols(
as_draws_df(peck_model_expectation) %>%
mutate(Hungry = exp(b_ExpectationHungry),
Satiated = exp(b_ExpectationSatiated)) %>%
select(Hungry, Satiated)) %>%
mutate(`Expectation Satiated / Baseline` = Satiated / Baseline_estimate,
`Expectation Hungry / Baseline` = Hungry / Baseline_estimate) %>%
select(contains("Expectation")) %>%
pivot_longer(cols = everything(), values_to = "estimate", names_to = "Stat") %>%
group_by(Stat) %>%
summarise_draws("median", "sd", ~quantile(.x, probs = c(0.025, 0.975), na.rm = TRUE), .cores = 4) %>%
select(-variable)
) %>%
pander()
| Bias treatment Hungry / Baseline |
7.883 |
4.111 |
3.728 |
19.04 |
| Bias treatment Satiated / Baseline |
6.713 |
3.562 |
3.149 |
16.48 |
| Expectation Hungry / Baseline |
8.67 |
4.554 |
4.108 |
20.94 |
| Expectation Satiated / Baseline |
5.722 |
3.065 |
2.688 |
14 |
Session info
sessionInfo() %>% pander
R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale: LC_COLLATE=English_Australia.utf8,
LC_CTYPE=English_Australia.utf8,
LC_MONETARY=English_Australia.utf8, LC_NUMERIC=C and
LC_TIME=English_Australia.utf8
attached base packages: grid,
stats, graphics, grDevices, utils,
datasets, methods and base
other attached packages: ggdag(v.0.2.10),
png(v.0.1-8), pander(v.0.6.5), DT(v.0.28),
patchwork(v.1.1.2), kableExtra(v.1.3.4),
MetBrewer(v.0.2.0), tidybayes(v.3.0.4),
loo(v.2.6.0), brms(v.2.19.0), Rcpp(v.1.0.11),
lubridate(v.1.9.2), forcats(v.1.0.0),
stringr(v.1.5.0), dplyr(v.1.1.2),
purrr(v.1.0.1), readr(v.2.1.4),
tidyr(v.1.3.0), tibble(v.3.2.1),
ggplot2(v.3.4.2) and tidyverse(v.2.0.0)
loaded via a namespace (and not attached):
tensorA(v.0.36.2), rstudioapi(v.0.15.0),
jsonlite(v.1.8.7), magrittr(v.2.0.3),
farver(v.2.1.1), rmarkdown(v.2.23),
vctrs(v.0.6.3), base64enc(v.0.1-3),
webshot(v.0.5.5), htmltools(v.0.5.5),
dagitty(v.0.3-1), distributional(v.0.3.2),
curl(v.5.0.1), sass(v.0.4.7),
StanHeaders(v.2.26.27), bslib(v.0.5.0),
htmlwidgets(v.1.6.2), plyr(v.1.8.8),
zoo(v.1.8-12), cachem(v.1.0.8),
igraph(v.1.5.0.1), mime(v.0.12),
lifecycle(v.1.0.3), pkgconfig(v.2.0.3),
colourpicker(v.1.2.0), Matrix(v.1.5-4.1),
R6(v.2.5.1), fastmap(v.1.1.1),
shiny(v.1.7.4.1), digest(v.0.6.33),
colorspace(v.2.1-0), ps(v.1.7.5),
crosstalk(v.1.2.0), labeling(v.0.4.2),
fansi(v.1.0.4), timechange(v.0.2.0),
polyclip(v.1.10-4), httr(v.1.4.6),
abind(v.1.4-5), compiler(v.4.3.1),
bit64(v.4.0.5), withr(v.2.5.0),
backports(v.1.4.1), inline(v.0.3.19),
shinystan(v.2.6.0), viridis(v.0.6.4),
highr(v.0.10), pkgbuild(v.1.4.2),
ggforce(v.0.4.1), MASS(v.7.3-60),
gtools(v.3.9.4), tools(v.4.3.1),
httpuv(v.1.6.11), threejs(v.0.3.3),
glue(v.1.6.2), callr(v.3.7.3),
nlme(v.3.1-162), promises(v.1.2.0.1),
checkmate(v.2.2.0), reshape2(v.1.4.4),
generics(v.0.1.3), gtable(v.0.3.3),
tzdb(v.0.4.0), hms(v.1.1.3),
tidygraph(v.1.2.3), xml2(v.1.3.5),
utf8(v.1.2.3), ggrepel(v.0.9.3),
pillar(v.1.9.0), ggdist(v.3.3.0),
markdown(v.1.7), vroom(v.1.6.3),
posterior(v.1.4.1), later(v.1.3.1),
tweenr(v.2.0.2), lattice(v.0.21-8),
bit(v.4.0.5), tidyselect(v.1.2.0),
miniUI(v.0.1.1.1), knitr(v.1.43),
arrayhelpers(v.1.1-0), gridExtra(v.2.3),
V8(v.4.3.3), svglite(v.2.1.1),
stats4(v.4.3.1), xfun(v.0.39),
graphlayouts(v.1.0.0), bridgesampling(v.1.1-2),
matrixStats(v.1.0.0), rstan(v.2.26.22),
stringi(v.1.7.12), boot(v.1.3-28.1),
yaml(v.2.3.7), evaluate(v.0.21),
codetools(v.0.2-19), ggraph(v.2.1.0),
cli(v.3.6.1), RcppParallel(v.5.1.7),
shinythemes(v.1.2.0), xtable(v.1.8-4),
systemfonts(v.1.0.4), munsell(v.0.5.0),
processx(v.3.8.2), jquerylib(v.0.1.4),
coda(v.0.19-4), svUnit(v.1.0.6),
parallel(v.4.3.1), rstantools(v.2.3.1.1),
ellipsis(v.0.3.2), prettyunits(v.1.1.1),
dygraphs(v.1.1.1.6), bayesplot(v.1.10.0),
Brobdingnag(v.1.2-9), viridisLite(v.0.4.2),
mvtnorm(v.1.2-2), scales(v.1.2.1),
xts(v.0.13.1), crayon(v.1.5.2),
rlang(v.1.1.1), rvest(v.1.0.3) and
shinyjs(v.2.1.0)
---
title: 'An undergraduate classroom experiment demonstrates the potential of subconscious bias to influence data collection in animal behaviour'
author: 'Thomas Keaney, Theresa Jones and Raoul Mulder' 
output:
  html_document:
    code_folding: hide
    depth: 1
    number_sections: no
    theme: yeti
    toc: yes
    toc_float: yes
    code_download: true
editor_options:
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, cache = FALSE)
```

# Teaching materials

$~$

**‘hungry forager’ primer**: 

**‘satiated forager’ primer**:

Pigeon footage is available on the github page associated with this report. It can be accessed here: https://github.com/tomkeaney/Biased_pigeons by downloading the `pigeon_video.mp4` file.

$~$

# Load packages and the data

```{r}
library(tidyverse) # tidy style coding
library(brms) # Bayesian models
library(loo) # for information criteria
library(tidybayes) # Bayesian aesthetics
library(MetBrewer) # colours
library(kableExtra) # tables
library(patchwork) # putting plots together
library(DT) # for search- and saveable tables
library(pander) # for simpler tables
library(png) # to load images
library(grid) # to plot images
library(ggdag) # to draw dags
```

```{r}

data <- read_csv("data/pigeon_data.csv") %>% 
  mutate(Student_ID = as.factor(Student_ID),
         Year = as.factor(Year),
         Foraging_prop = (Foraging_percentage / 100)) %>%
  filter(Year %in% c("2021", "2022", "2023"),
         Foraging_percentage != "NA",
         Primer_understood != "NA") %>%
  filter(Student_ID != "60" & Student_ID != "68" & Student_ID != "70" & Student_ID != "72") %>% # remove students that completed the task multiple times
  #Student_ID %in% c("60", "68", "70", "72")) %>% 
  select(-c(First_name, Surname, Peck_mean)) %>%  # remove names when ready
  left_join(
    read_csv("data/gender_data.csv") %>% 
      mutate(Student_ID = as.factor(Student_ID))
  ) %>% 
  rename(Observer_ID = Student_ID) %>% 
  select(Observer_ID, Year, Gender, everything())

data_peck <- 
  data %>%
  filter(Peck_rate_2 != "NA") %>% 
  pivot_longer(cols = Peck_rate_1:Peck_rate_2, names_to = "Trial",
               values_to = "Peck_rate")
  
# Create a function to build HTML searchable tables

my_data_table <- function(df){
  datatable(
    df, rownames=FALSE,
    autoHideNavigation = TRUE,
    extensions = c("Scroller",  "Buttons"),
    options = list(
      dom = 'Bfrtip',
      deferRender=TRUE,
      scrollX=TRUE, scrollY=400,
      scrollCollapse=TRUE,
      buttons =
        list('pageLength', 'colvis', 'csv', list(
          extend = 'pdf',
          pageSize = 'A4',
          orientation = 'landscape',
          filename = 'full_dataset')),
      pageLength = 78
    )
  )
}

my_data_table(data)
```

**Column explanations**

**Observer_ID**: unique, anonymised identifier for each observer.

**Year**: year that the experiment was conducted.

**Gender**: upon enrollment at The University of Melbourne, students are asked to indicate their title. We identified women as observers that answered "Miss" or "Ms" and Men as those who that answered "Mr". Those with entirely missing entries were coded as "NA".  

**Bias_treatment**: the primer the observer received, where 'satiated' indicates that the observers were provided information prior to a trial that suggested pigeons were fell fed, whereas 'hungry' indicated that the pigeons were in poor condition and hungry.

**Expectation**: we asked the observers to indicate whether they thought the pigeons would be hungry or satiated. We included this question to test whether observers were appropriately primed by their bias treatment.

**Primer_understood**: did the observer's expectation match the primer they received?

**Foraging_percentage**: the percentage of pigeons that observers estimated to be foraging over a 15 second period, while observing a large flock.

**Peck_rate_1**: the number of times a single chosen pigeon pecked the ground over a 15 second period.

**Peck_rate_2**: the number of times a second chosen pigeon pecked the ground over a 15 second period.

**Foraging_prop**: proportion of pigeons estimated to be foraging 

$~$

# Question 1: is estimation of foraging percentage affected by bias manipulations?

$~$

## Exploring allocation versus expectation 1 {.tabset .tabset-pills}

$~$

We find that **22 of the 78 observers** indicated a feeding motivation expectation opposite to that implied by the primer they were allocated. 

This suggests that `Expectation` may be a better predictor of foraging estimation than allocated `Bias_treatment`. The relationship between these and all other variables that we expect to play a role in this system are depicted in Figure 1.

We explicitly assess the effect of treatment and expectation by fitting two models:

1. a model with allocated primer (`Bias_treatment`) as the predictor variable 

2. a model with indicated hunger expectation (`Expectation`) as the predictor variable

$~$

```{r, include=FALSE}
gg_simple_dag <- function(d) {
  d %>% 
    ggplot(aes(x = x, y = y, xend = xend, yend = yend, colour = Variables)) +
    geom_dag_point() +
    scale_colour_manual(values = c("Included in model" = met.brewer("Hiroshige")[4], "Not included in model" = "grey80")) +
    geom_dag_text(color = met.brewer("Hiroshige")[7]) +
    geom_dag_edges() + 
    theme_dag()+
    theme(legend.position = "bottom",
          legend.title = element_blank())
}


observer_bias_dag <- dagify(EF ~ PEW + PEM + SB + TF,
        PEW ~ BT,
        PEM ~ BT,
        SB ~ PEW + PEM,
       labels = c("TF" = "True\n Foraging", 
                  "EF" = "Estimated\n Foraging",
                  "PEW" = "Prior\n Expectation Men",
                  "PEM" = "Prior\n Expectation Women",
                  "BT" = "Bias\n Treatment",
                  "SB" = "Selection\n Bias")) %>% 
  tidy_dagitty(seed = 5)
  
  
 observer_bias_dag <- left_join(observer_bias_dag$data, tibble(name = c("BT", "EF", "PEW", "PEM", "SB", "TF"),
                                   Variables = c("Included in model", "Included in model", "Included in model", "Included in model", "Not included in model", "Not included in model"))) %>% 
   
 gg_simple_dag()


observer_bias_dag

#**Figure 1**. A direct acrylic diagram showing the flow of causation in our biological system. Our bias treatment (BT) was designed to affect the prior expectation (PE) of observer's, a subset of which were women (PEW), while the remaining were men (PEM). Prior expectations may affect the estimated level of foraging (EF) directly, or more specifically through biased selection of particular foragers (SB). Estimated foraging is also affected by the true level of foraging (TF) carried out by the flock of pigeons in the footage. We hypothesised that _a priori_ expectations of observer's would affect their foraging estimates. The paths connecting coloured variables show that this can be tested by modelling the effect of bias treatment on estimated foraging, or alternatively by directly modelling the effect of prior expectation on estimated foraging. 


```


$~$

### Model 1: allocated primer

```{r}

# First let's model the effect of bias treatment on foraging estimation 

foraging_model_treatment <- brm(Foraging_prop ~ 0 + Bias_treatment,
                                     data = data, family = Beta,
                                     prior = c(prior(normal(0, 1.5), class = b),
                                               prior(exponential(1), class = phi)),
                                     iter = 6000, warmup = 2000, chains = 4, cores = 4,
                                     control = list(adapt_delta = 0.8, max_treedepth = 10),
                                     seed = 1, file = "fits/foraging_model_treatment")

foraging_model_treatment <- add_criterion(foraging_model_treatment, criterion = "loo", file = "fits/foraging_model_treatment")

foraging_model_treatment

foraging_model_treatment_gender <- brm(Foraging_prop ~ 0 + Gender * Bias_treatment,
                                     data = data, family = Beta,
                                     prior = c(prior(normal(0, 1.5), class = b),
                                               prior(exponential(1), class = phi)),
                                     iter = 6000, warmup = 2000, chains = 4, cores = 4,
                                     control = list(adapt_delta = 0.8, max_treedepth = 10),
                                     seed = 1, file = "fits/foraging_model_treatment_gender")

```

**Table S1**. Posterior estimates of the percentage of pigeon feeding rate, split by the primer observers were allocated.

```{r}
new_data <- tibble(Bias_treatment = c("Hungry", "Satiated"))

new_data %>% 
  left_join(data %>% group_by(Bias_treatment) %>% summarise(`n observers` = n())) %>% 
  cbind(fitted(foraging_model_treatment, newdata = new_data, summary = T) %>% 
          as_tibble() %>% 
          mutate(across(1:4, ~ .x *100),
                 across(1:4, round, 2))) %>% 
  rename("Estimated proportion foraging" = Estimate,
         "Bias treatment" = Bias_treatment) %>% 
  pander(split.cell = 20, split.table = Inf)

```


```{r, include=FALSE}
# add gender predictions

new_data_gender <- expand_grid(Bias_treatment = c("Hungry", "Satiated"),
                               Gender = c("Woman", "Man"))

new_data_gender %>% 
  left_join(data %>% group_by(Gender, Bias_treatment) %>% summarise(`n observers` = n()) %>% 
          ungroup() %>% filter(!is.na(Gender))) %>% 
  cbind(fitted(foraging_model_treatment_gender, newdata = new_data_gender, summary = T) %>% 
          as_tibble() %>% 
          mutate(across(1:4, ~ .x *100),
                 across(1:4, round, 2))) %>% 
  rename("Estimated proportion foraging" = Estimate,
         "Bias treatment" = Bias_treatment) %>% 
  pander(split.cell = 20, split.table = Inf)
```


### Model 2: indicated expectation

```{r}

# fit the same model, except using participant expectation rather than allocated bias treatment

foraging_model_expectation <- brm(Foraging_prop ~ 0 + Expectation,
                                     data = data, family = Beta,
                                     prior = c(prior(normal(0, 1.5), class = b),
                                               prior(exponential(1), class = phi)),
                                     iter = 6000, warmup = 2000, chains = 4, cores = 4,
                                     control = list(adapt_delta = 0.8, max_treedepth = 10),
                                     seed = 1, file = "fits/foraging_model_expectation")

foraging_model_expectation <- add_criterion(foraging_model_expectation, criterion = "loo", file = "fits/foraging_model_expectation")

foraging_model_expectation

foraging_model_expectation_gender <- brm(Foraging_prop ~ 0 + Gender * Expectation,
                                     data = data, family = Beta,
                                     prior = c(prior(normal(0, 1.5), class = b),
                                               prior(exponential(1), class = phi)),
                                     iter = 6000, warmup = 2000, chains = 4, cores = 4,
                                     control = list(adapt_delta = 0.8, max_treedepth = 10),
                                     seed = 1, file = "fits/foraging_model_expectation_gender")

```

**Table S2**. Posterior estimates of the percentage of pigeons foraging, split by the actual expectation of observers.

```{r}

new_data_2 <- tibble(Expectation = c("Hungry", "Satiated"))

new_data_2 %>% 
   left_join(data %>% group_by(Expectation) %>% summarise(`n observers` = n())) %>% 
  cbind(fitted(foraging_model_expectation, newdata = new_data_2, summary = T) %>% 
          as_tibble() %>% 
          mutate(across(1:4, ~ .x *100),
                 across(1:4, round, 2))) %>%  
  rename("Estimated proportion foraging" = Estimate,
         "Indicated expectation" = Expectation) %>% 
  pander(split.cell = 20, split.table = Inf)

```

```{r, include=FALSE}
# add gender predictions

new_data_gender_2 <- expand_grid(Expectation = c("Hungry", "Satiated"),
                               Gender = c("Woman", "Man"))

new_data_gender_2 %>% 
  left_join(data %>% group_by(Expectation, Gender) %>% summarise(`n observers` = n()) %>% 
          ungroup() %>% filter(!is.na(Gender))) %>%
  cbind(fitted(foraging_model_expectation_gender, 
               newdata = new_data_gender_2, summary = T) %>% 
          as_tibble() %>% 
          mutate(across(1:4, ~ .x *100),
                 across(1:4, round, 2))) %>% 
  rename("Estimated proportion foraging" = Estimate,
         "Indicated expectation" = Expectation) %>% 
  pander(split.cell = 20, split.table = Inf)
```


$~$

## Build Figure 1a-d

$~$

**Get posterior means and difference contrasts**

```{r}

# treatment model

draws_treatment <-
  as_draws_df(foraging_model_treatment) %>% 
  mutate(Hungry = inv_logit_scaled(b_Bias_treatmentHungry) *100,
         Satiated = inv_logit_scaled(b_Bias_treatmentSatiated)*100,
         diff_contrast = (Hungry - Satiated)) %>% 
  select(Hungry, Satiated, diff_contrast) %>% 
  pivot_longer(names_to = "Treatment", values_to = "Posterior_estimate", cols = 1:3) %>% 
  mutate(Predictor = "Allocated primer")

p2 <- 
  draws_treatment %>% 
  filter(Treatment != "diff_contrast") %>% 
  ggplot(aes(x = Treatment, y = Posterior_estimate)) + 
  stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 4, stroke = 1.5) +
  scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  coord_flip(ylim = c(25, 55)) +
  #geom_vline(xintercept = 0, linetype = 2) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  xlab("Allocated primer") +
  ylab("Estimated % pigeons foraging") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        text = element_text(size = 14))

p3 <-
  draws_treatment %>% 
  filter(Treatment == "diff_contrast") %>% 
  ggplot(aes(y = Posterior_estimate)) + 
   stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 4, stroke = 1.5, scale =0.5) +
  scale_fill_manual(values = met.brewer("Hiroshige")[4]) +
  coord_flip(ylim = c(-20, 20)) +
  geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  xlab(NULL) +
  ylab("Hungry - Satiated difference\ncontrast (% points)") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        text = element_text(size = 14))

# expectation model

draws_expectation <-
  as_draws_df(foraging_model_expectation) %>% 
  mutate(Hungry = inv_logit_scaled(b_ExpectationHungry) *100,
         Satiated = inv_logit_scaled(b_ExpectationSatiated)*100,
         diff_contrast = (Hungry - Satiated)) %>% 
  select(Hungry, Satiated, diff_contrast) %>% 
  pivot_longer(names_to = "Treatment", values_to = "Posterior_estimate", cols = 1:3) %>% 
  mutate(Predictor = "Indicated expectation")

p4 <-
  draws_expectation %>% 
  filter(Treatment != "diff_contrast") %>% 
  ggplot(aes(x = Treatment, y = Posterior_estimate)) + 
   stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 4, stroke = 1.5) +
  scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  coord_flip(ylim = c(25, 55)) +
  #geom_vline(xintercept = 0, linetype = 2) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  xlab("Indicated expectation") +
  ylab("Estimated % pigeons foraging") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        text = element_text(size = 14))

p5 <-
  draws_expectation %>% 
  filter(Treatment == "diff_contrast") %>% 
  ggplot(aes(y = Posterior_estimate)) + 
    stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 4, stroke = 1.5, scale =0.5) +
  scale_fill_manual(values = met.brewer("Hiroshige")[4]) +
  coord_flip(ylim = c(-20, 20)) +
  geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  xlab(NULL) +
  ylab("Hungry - Satiated difference\ncontrast (% points)") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        text = element_text(size = 14))
```

```{r, include=FALSE}

# treatment model

gender_treatment_draws <-
  fitted(foraging_model_treatment_gender, 
         newdata = new_data_gender, summary = F) %>% 
  as_tibble() %>% 
  rename(Hungry_Women = V1, Hungry_Men = V2, Satiated_Women = V3, Satiated_Men = V4) %>% 
  pivot_longer(cols = 1:4, names_to = "Group", values_to = "Posterior_estimate") %>% 
  separate(sep = "_", col = Group, into = c("Treatment", "Gender")) %>%
  mutate(Posterior_estimate = Posterior_estimate*100) 
  
calculate_all_the_diffs <-
  fitted(foraging_model_treatment_gender, 
         newdata = new_data_gender, summary = F) %>% 
  as_tibble() %>% 
  rename(Hungry_Woman = V1, Hungry_Man = V2, Satiated_Woman = V3, Satiated_Man = V4) %>% 
  mutate(Woman_h_s_diff = Hungry_Woman - Satiated_Woman,
         Man_h_s_diff = Hungry_Man - Satiated_Man,
         diff_diff = Woman_h_s_diff - Man_h_s_diff) %>% 
  select(contains("diff")) %>%
  rename(`H-S (women)` = Woman_h_s_diff,
         `H-S (men)` = Man_h_s_diff,
         `Interaction` = diff_diff) %>% 
  pivot_longer(cols = 1:3, names_to = "diff_contrast", values_to = "posterior_diff") %>% 
  mutate(posterior_diff = posterior_diff*100)
  

gp1 <- 
  gender_treatment_draws %>% 
  ggplot(aes(x = Gender, y = Posterior_estimate)) + 
    stat_slab(alpha = 0.8, shape = 21, aes(fill = Treatment)) +
  #stat_halfeye(.width = c(0.66, 0.95), alpha = 0.9,
   #            point_interval = "median_qi", point_fill = "white",
   #            shape = 21, point_size = 4, stroke = 1.5,
   #            fill = met.brewer("Hiroshige", 5)[2]) +
  #scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  scale_fill_manual(values = c(met.brewer("Hiroshige", 10)[4], met.brewer("Hiroshige", 10)[6])) +
  coord_flip()+#ylim = c(25, 55)) +
  #geom_vline(xintercept = 0, linetype = 2) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  labs(x = "Gender", y = "Estimated % pigeons foraging", fill = "Allocated\nprimer") +
  theme_bw() + 
  theme(legend.position = "bottom",
        panel.grid.minor = element_blank(),
        text = element_text(size = 12),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10))



gp2 <-
  calculate_all_the_diffs %>% 
  ggplot(aes(x = diff_contrast, y = posterior_diff)) + 
    #stat_slab(alpha = 0.9, shape = 21, aes(fill = Gender)) +
  stat_halfeye(.width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 3, stroke = 1.5,
               fill = met.brewer("Hiroshige", 10)[5]) +
  #scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  coord_flip()+#ylim = c(25, 55)) +
   geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
  #geom_vline(xintercept = 0, linetype = 2) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  xlab("Difference contrast") +
  ylab("% points") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        text = element_text(size = 12))

# expectation model

gender_expectation_draws <-
  fitted(foraging_model_expectation_gender, 
         newdata = new_data_gender_2, summary = F) %>% 
  as_tibble() %>% 
  rename(Hungry_Women = V1, Hungry_Men = V2, Satiated_Women = V3, Satiated_Men = V4) %>% 
  pivot_longer(cols = 1:4, names_to = "Group", values_to = "Posterior_estimate") %>% 
  separate(sep = "_", col = Group, into = c("Treatment", "Gender")) %>%
  mutate(Posterior_estimate = Posterior_estimate*100) 
  
calculate_all_the_diffs_2 <-
  fitted(foraging_model_expectation_gender, 
         newdata = new_data_gender_2, summary = F) %>% 
  as_tibble() %>% 
  rename(Hungry_Woman = V1, Hungry_Man = V2, Satiated_Woman = V3, Satiated_Man = V4) %>% 
  mutate(Woman_h_s_diff = Hungry_Woman - Satiated_Woman,
         Man_h_s_diff = Hungry_Man - Satiated_Man,
         diff_diff = Woman_h_s_diff - Man_h_s_diff) %>% 
  select(contains("diff")) %>%
  rename(`H-S (women)` = Woman_h_s_diff,
         `H-S (men)` = Man_h_s_diff,
         `Interaction` = diff_diff) %>% 
  pivot_longer(cols = 1:3, names_to = "diff_contrast", values_to = "posterior_diff") %>% 
  mutate(posterior_diff = posterior_diff*100)

gp3 <- 
  gender_expectation_draws %>% 
  ggplot(aes(x = Gender, y = Posterior_estimate)) + 
    stat_slab(alpha = 0.9, shape = 21, aes(fill = Treatment)) +
  #stat_halfeye(.width = c(0.66, 0.95), alpha = 0.9,
   #            point_interval = "median_qi", point_fill = "white",
   #            shape = 21, point_size = 4, stroke = 1.5,
   #            fill = met.brewer("Hiroshige", 5)[2]) +
  scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  coord_flip()+#ylim = c(25, 55)) +
  #geom_vline(xintercept = 0, linetype = 2) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  labs(x = "Gender", y = "Estimated % pigeons foraging", fill = "Indicated\nexpectation") +
  theme_bw() + 
    theme(legend.position = "bottom",
        panel.grid.minor = element_blank(),
        text = element_text(size = 12),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10))



gp4 <-
  calculate_all_the_diffs_2 %>% 
  ggplot(aes(x = diff_contrast, y = posterior_diff)) + 
    #stat_slab(alpha = 0.9, shape = 21, aes(fill = Gender)) +
  stat_halfeye(.width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 3, stroke = 1.5,
               fill = met.brewer("Hiroshige", 10)[5]) +
  #scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  coord_flip()+#ylim = c(25, 55)) +
   geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
  #geom_vline(xintercept = 0, linetype = 2) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  xlab("Difference contrast") +
  ylab("% points") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        text = element_text(size = 12))

```

$~$

# Question 2: is estimation of feeding rate affected by bias manipulations?

$~$

We asked observers to count the number of pecks of the ground that a selected pigeon made over a 1 minute period. We use the number of pecks that occur as a measure of feeding rate.

We first estimated a baseline peck rate by observing 35 pigeons. To select the pigeons we observed, we split a still image of the foraging video (taken at time zero) into a 43 x 21 cell grid. From the 122 cells that contained pigeons, 40 were chosen by random number generation (see code chunk below). In the event that multiple pigeons were present in the cell, we selected the most prominent to observe. Five observations were discarded - three due to overlap of the same pigeon between cells that were selected by the random number generator, and two more as the pigeons left the field of view during the video and could no longer be tracked. 


```{r}
# curly brackets run all lines included within them

{set.seed(1) # so that sample produces a reproducible sequence 
  sample(1:122, 40, replace = FALSE)}
```

The selected pigeons for baseline observation are shown in the image below

```{r}
img <- readPNG("pigeon_selection.png")
 grid.raster(img)
```


$~$

## Estimating baseline feeding rate

$~$

**Load in the data**

```{r}

baseline_data <- read_csv("data/baseline_peck_data.csv") %>% 
  select(1:5) %>%  # remove the comments column
  pivot_longer(cols = 4:5, names_to = "Observation", values_to = "Peck_rate") %>% 
  mutate(ID = as.factor(ID),
         Observation = str_remove(Observation, "Peck_count_")) %>% 
  rename(Pigeon_ID = ID) %>% 
  filter(!is.na(Peck_rate))

my_data_table <- function(df){
  datatable(
    df, rownames=FALSE,
    autoHideNavigation = TRUE,
    extensions = c("Scroller",  "Buttons"),
    options = list(
      dom = 'Bfrtip',
      deferRender=TRUE,
      scrollX=TRUE, scrollY=400,
      scrollCollapse=TRUE,
      buttons =
        list('pageLength', 'colvis', 'csv', list(
          extend = 'pdf',
          pageSize = 'A4',
          orientation = 'landscape',
          filename = 'baseline_dataset')),
      pageLength = 78
    )
  )
}


my_data_table(baseline_data)

```

- `X` and `Y` represent grid coordinates.

- `Pigeon_ID` identifies a specific pigeon

- `Observation` indicates whether this was the first or second scoring for a single pigeon. We scored each pigeon twice as distant pigeons were difficult to observe and to ensure that the correct pigeon was tracked throughout the minute of observation.

- `Peck_rate` is the number of times the ground was pecked over a minute of observation.

$~$

**Fit a simple model to estimate median peck rate**

```{r}

baseline_peck_model_zi <-
  brm(Peck_rate ~ 1 + (1|Pigeon_ID),
      family = zero_inflated_negbinomial(), data = baseline_data,
      prior = c( prior(normal(0, 1.5), class = Intercept),
                 prior(exponential(1), class = sd),
                 prior(exponential(1), class = shape),
                 prior(exponential(1), class = zi)),
      chains = 4, cores = 4, warmup = 2000, iter = 6000,
      file = "fits/baseline_peck_model")

# wrangle the output

baseline_peck_predictions <-
  baseline_peck_model_zi %>% 
  as_draws_df() %>% 
  mutate(Baseline_estimate = exp(b_Intercept),
         peck_rate_sd = exp(sd_Pigeon_ID__Intercept)) %>% 
  select(Baseline_estimate, peck_rate_sd)

baseline_data %>% 
  distinct(Pigeon_ID) %>% 
  summarise(`n pigeons observed` = length(Pigeon_ID)) %>%
  bind_cols(
fitted(baseline_peck_model_zi, summary = T, re_formula = NA) %>% 
          as_tibble() %>%
          distinct(Estimate, .keep_all = T) %>% 
          mutate(across(1:4, round, 2)) %>% 
  rename(`Baseline median peck rate / per min` = Estimate)) %>%
  pander()

```

$~$

## Experimental data

$~$

### Exploring allocation versus expectation 2.0 {.tabset .tabset-pills}

$~$

Once again, we expect that attention paid to and/or comprehension of the primer statement has a large effect on observers' perception of pigeon foraging. 

Lets again fit our two models:

1. a model with allocated primer (`Bias_treatment`) as the predictor variable 

2. a model with indicated hunger expectation (`Expectation`) as the predictor variable

$~$

#### Model 1: allocated primer

```{r}

# First let's model the effect of bias treatment on peck rate

peck_model_treatment <- 
  brm(Peck_rate ~ 0 + Bias_treatment + (1|Observer_ID),
      data = data_peck, family = negbinomial,
      prior = c(prior(normal(0, 1.5), class = b),
                prior(exponential(1), class = sd)),
      iter = 6000, warmup = 2000, chains = 4, cores = 4,
      control = list(adapt_delta = 0.9, max_treedepth = 12),
      seed = 1, file = "fits/peck_model_treatment")

peck_model_treatment <- add_criterion(peck_model_treatment, criterion = "loo", file = "fits/peck_model_treatment")
  
peck_model_treatment

peck_model_treatment_gender <- 
  brm(Peck_rate ~ 0 + Gender * Bias_treatment + (1|Observer_ID),
      data = data_peck, family = negbinomial,
      prior = c(prior(normal(0, 1.5), class = b),
                prior(exponential(1), class = sd)),
      iter = 6000, warmup = 2000, chains = 4, cores = 4,
      control = list(adapt_delta = 0.9, max_treedepth = 12),
      seed = 1, file = "fits/peck_model_treatment_gender")
```

**Table S3**. The estimated peck rate of foraging pigeons, split by the primer observers were allocated.

```{r}
new_data %>% 
  bind_cols(fitted(peck_model_treatment, newdata = new_data, summary = T, re_formula = NA) %>% 
              as_tibble() %>% 
              mutate(across(1:4, round, 2))) %>% 
  left_join(data_peck %>% group_by(Bias_treatment) %>% 
              distinct(Observer_ID) %>% summarise(`n pigeons observed` = n())) %>% 
  rename("Estimated peck rate" = Estimate,
         "Bias treatment" = Bias_treatment) %>% 
  bind_rows(fitted(baseline_peck_model_zi, summary = T, re_formula = NA) %>% 
              as_tibble() %>%
              distinct(Estimate, .keep_all = T) %>% 
              mutate(across(1:4, round, 2)) %>% 
              rename("Estimated peck rate" = Estimate) %>% 
              mutate(`Bias treatment` = "Baseline") %>% 
              bind_cols(baseline_data %>% 
                          distinct(Pigeon_ID) %>% 
                          summarise(`n pigeons observed` = length(Pigeon_ID)))) %>% 
  select(`Bias treatment`, `n pigeons observed`, everything()) %>% 
  pander(split.cell = 20, split.table = Inf)

```

```{r, include=FALSE}
# add the gender table

new_data_gender %>% 
  bind_cols(fitted(peck_model_treatment_gender, 
               newdata = new_data_gender, summary = T, re_formula = NA) %>% 
          as_tibble() %>% 
          mutate(across(1:4, round, 2))) %>% 
  left_join(data_peck %>% group_by(Bias_treatment, Gender) %>% 
               distinct(Observer_ID) %>% summarise(`n pigeons observed` = n())) %>% 
    rename("Estimated peck rate" = Estimate,
         "Bias treatment" = Bias_treatment) %>% 
  bind_rows(fitted(baseline_peck_model_zi, summary = T, re_formula = NA) %>% 
              as_tibble() %>%
              distinct(Estimate, .keep_all = T) %>% 
              mutate(across(1:4, round, 2)) %>% 
              rename("Estimated peck rate" = Estimate) %>% 
              mutate(`Bias treatment` = "Baseline", Gender = "-") %>% 
              bind_cols(baseline_data %>% 
                          distinct(Pigeon_ID) %>% 
                          summarise(`n pigeons observed` = length(Pigeon_ID)))) %>% 
  select(`Bias treatment`, `n pigeons observed`, everything()) %>% 
  pander(split.cell = 20, split.table = Inf)
```


#### Model 2: Indicated expectation

```{r}

# fit the same model, except using participant expectation rather than allocated bias treatment

peck_model_expectation <- brm(Peck_rate ~ 0 + Expectation + (1|Observer_ID),
                                     data = data_peck, family = negbinomial,
                                     prior = c(prior(normal(0, 1.5), class = b),
                                               prior(exponential(1), class = sd)),
                                     iter = 6000, warmup = 2000, chains = 4, cores = 4,
                                     control = list(adapt_delta = 0.95, max_treedepth = 12),
                                     seed = 1, file = "fits/peck_model_expectation")

peck_model_expectation <- add_criterion(peck_model_expectation, criterion = "loo", file = "fits/peck_model_expectation")


peck_model_expectation
#loo_compare(peck_model_treatment, peck_model_expectation)

peck_model_expectation_gender <- brm(Peck_rate ~ 0 + Gender * Expectation + (1|Observer_ID),
                                     data = data_peck, family = negbinomial,
                                     prior = c(prior(normal(0, 1.5), class = b),
                                               prior(exponential(1), class = sd)),
                                     iter = 6000, warmup = 2000, chains = 4, cores = 4,
                                     control = list(adapt_delta = 0.95, max_treedepth = 12),
                                     seed = 1, file = "fits/peck_model_expectation_gender")
```

**Table S4**. The estimated peck rate of foraging pigeons, split by the indicated expectation of the observers.

```{r}

new_data_2 %>% 
  bind_cols(fitted(peck_model_expectation, newdata = new_data_2, summary = T, re_formula = NA) %>% 
              as_tibble() %>% 
              mutate(across(1:4, round, 2))) %>% 
  left_join(data_peck %>% group_by(Expectation) %>% 
              distinct(Observer_ID) %>% summarise(`n pigeons observed` = n())) %>% 
  rename("Estimated peck rate" = Estimate,
         "Indicated expectation" = Expectation) %>% 
  bind_rows(fitted(baseline_peck_model_zi, summary = T, re_formula = NA) %>% 
              as_tibble() %>%
              distinct(Estimate, .keep_all = T) %>% 
              mutate(across(1:4, round, 2)) %>% 
              rename("Estimated peck rate" = Estimate) %>% 
              mutate(`Indicated expectation` = "Baseline") %>% 
              bind_cols(baseline_data %>% 
                          distinct(Pigeon_ID) %>% 
                          summarise(`n pigeons observed` = length(Pigeon_ID)))) %>% 
  select(`Indicated expectation`, `n pigeons observed`, everything()) %>% 
  pander(split.cell = 20, split.table = Inf)

```

```{r, include=FALSE}

# add the gender table

new_data_gender_2 %>% 
  bind_cols(fitted(peck_model_expectation_gender, 
               newdata = new_data_gender_2, summary = T, re_formula = NA) %>% 
          as_tibble() %>% 
          mutate(across(1:4, round, 2))) %>% 
  left_join(data_peck %>% group_by(Expectation, Gender) %>% 
               distinct(Observer_ID) %>% summarise(`n pigeons observed` = n())) %>% 
    rename("Estimated peck rate" = Estimate,
         "Indicated expectation" = Expectation) %>% 
  bind_rows(fitted(baseline_peck_model_zi, summary = T, re_formula = NA) %>% 
              as_tibble() %>%
              distinct(Estimate, .keep_all = T) %>% 
              mutate(across(1:4, round, 2)) %>% 
              rename("Estimated peck rate" = Estimate) %>% 
              mutate(`Indicated expectation` = "Baseline", Gender = "-") %>% 
              bind_cols(baseline_data %>% 
                          distinct(Pigeon_ID) %>% 
                          summarise(`n pigeons observed` = length(Pigeon_ID)))) %>% 
  select(`Indicated expectation`, `n pigeons observed`, everything()) %>% 
  pander(split.cell = 20, split.table = Inf)
```


$~$

### Build Figure 1e-h

$~$

**Get posterior means and difference contrasts**

```{r}

# treatment model

peck_draws_treatment <-
  as_draws_df(peck_model_treatment) %>% 
  mutate(Hungry = exp(b_Bias_treatmentHungry),
         Satiated = exp(b_Bias_treatmentSatiated),
         diff_contrast = (Hungry - Satiated)) %>% 
  select(Hungry, Satiated, diff_contrast) %>%
  bind_cols(baseline_peck_predictions %>% select(Baseline_estimate)) %>% 
  pivot_longer(names_to = "Treatment", values_to = "Posterior_estimate", cols = 1:3) %>% 
  mutate(Predictor = "Allocated primer")


p6 <- 
    peck_draws_treatment %>% 
    filter(Treatment != "diff_contrast") %>% 
    ggplot(aes(x = Treatment, y = Posterior_estimate)) +
    stat_slab(aes(y = Baseline_estimate),
              linetype = 2, linewidth = 0.8, slab_fill = "white",
              colour = "black") +
    stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
                 point_interval = "median_qi", point_fill = "white",
                 shape = 21, point_size = 4, stroke = 1.5) +
    scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
    coord_flip(ylim = c(0, 20)) +
    #geom_vline(xintercept = 0, linetype = 2) +
    #scale_y_continuous(breaks = c(, 0, 1)) +
    xlab("Allocated primer") +
    ylab("Estimated pecks per min") +
    theme_bw() + 
    theme(legend.position = "none",
          panel.grid.minor = element_blank(),
          text = element_text(size = 14))

p7 <-
  peck_draws_treatment %>% 
  filter(Treatment == "diff_contrast") %>% 
  ggplot(aes(y = Posterior_estimate)) + 
   stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 4, stroke = 1.5, scale =0.5) +
  scale_fill_manual(values = met.brewer("Hiroshige")[4]) +
  coord_flip(ylim = c(-5, 12)) +
  geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  xlab(NULL) +
  ylab("Hungry - Satiated difference\ncontrast (pecks per min)") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        text = element_text(size = 14))

# expectation model

peck_draws_expectation <-
  as_draws_df(peck_model_expectation) %>% 
  mutate(Hungry = exp(b_ExpectationHungry),
         Satiated = exp(b_ExpectationSatiated),
         diff_contrast = (Hungry - Satiated)) %>% 
  select(Hungry, Satiated, diff_contrast) %>% 
  bind_cols(baseline_peck_predictions %>% select(Baseline_estimate)) %>% 
  pivot_longer(names_to = "Treatment", values_to = "Posterior_estimate", cols = 1:3) %>% 
  mutate(Predictor = "Allocated primer")

p8 <-
  peck_draws_expectation %>% 
  filter(Treatment != "diff_contrast") %>% 
  ggplot(aes(x = Treatment, y = Posterior_estimate)) + 
  stat_slab(aes(y = Baseline_estimate),
              linetype = 2, linewidth = 0.8, slab_fill = "white",
              colour = "black") +
    stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 4, stroke = 1.5) +
  scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  coord_flip(ylim = c(0, 20)) +
  #geom_vline(xintercept = 0, linetype = 2) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  xlab("Indicated expectation") +
  ylab("Estimated pecks per min") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        text = element_text(size = 14))

p9 <-
  peck_draws_expectation %>% 
  filter(Treatment == "diff_contrast") %>% 
  ggplot(aes(y = Posterior_estimate)) + 
   stat_halfeye(aes(fill = Treatment), .width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 4, stroke = 1.5, scale =0.5) +
  scale_fill_manual(values = met.brewer("Hiroshige")[4]) +
  coord_flip(ylim = c(-5, 12)) +
  geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  xlab(NULL) +
  ylab("Hungry - Satiated difference\ncontrast (pecks per min)") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        text = element_text(size = 14))
```

```{r, include=FALSE}
# with gender

gender_treatment_draws_2 <-
  fitted(peck_model_treatment_gender, 
         newdata = new_data_gender, summary = F, re_formula = NA) %>% 
  as_tibble() %>% 
  rename(Hungry_Women = V1, Hungry_Men = V2, Satiated_Women = V3, Satiated_Men = V4) %>% 
  bind_cols(baseline_peck_predictions %>% select(Baseline_estimate)) %>% 
  pivot_longer(names_to = "Group", values_to = "Posterior_estimate", cols = 1:4) %>% 
  separate(sep = "_", col = Group, into = c("Treatment", "Gender"))
  
calculate_all_the_diffs_3 <-
  fitted(peck_model_treatment_gender, 
         newdata = new_data_gender, summary = F, re_formula = NA) %>% 
  as_tibble() %>% 
  rename(Hungry_Women = V1, Hungry_Men = V2, Satiated_Women = V3, Satiated_Men = V4) %>% 
  mutate(Women_h_s_diff = Hungry_Women - Satiated_Women,
         Men_h_s_diff = Hungry_Men - Satiated_Men,
         diff_diff = Women_h_s_diff - Men_h_s_diff) %>% 
  select(contains("diff")) %>%
  rename(`H-S (women)` = Women_h_s_diff,
         `H-S (men)` = Men_h_s_diff,
         `Interaction` = diff_diff) %>% 
  pivot_longer(cols = 1:3, names_to = "diff_contrast", values_to = "posterior_diff")

gp5 <- 
  gender_treatment_draws_2 %>% 
  ggplot(aes(x = Gender, y = Posterior_estimate)) + 
    stat_slab(alpha = 0.8, shape = 21, aes(fill = Treatment)) +
  stat_slab(aes(y = Baseline_estimate),
              linetype = 2, linewidth = 0.8, slab_fill = "white",
              colour = "black") +
  #stat_halfeye(.width = c(0.66, 0.95), alpha = 0.9,
   #            point_interval = "median_qi", point_fill = "white",
   #            shape = 21, point_size = 4, stroke = 1.5,
   #            fill = met.brewer("Hiroshige", 5)[2]) +
  scale_fill_manual(values = c(met.brewer("Hiroshige", 10)[4], met.brewer("Hiroshige", 10)[6])) +
  coord_flip()+#ylim = c(25, 55)) +
  #geom_vline(xintercept = 0, linetype = 2) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  labs(x = "Gender", y = "Estimated pecks per min", fill = "Allocated\nPrimer") +
  theme_bw() + 
    theme(legend.position = "bottom",
        panel.grid.minor = element_blank(),
        text = element_text(size = 12),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10))



gp6 <-
  calculate_all_the_diffs_3 %>% 
  ggplot(aes(x = diff_contrast, y = posterior_diff)) + 
    #stat_slab(alpha = 0.9, shape = 21, aes(fill = Gender)) +
  stat_halfeye(.width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 3, stroke = 1.5,
               fill = met.brewer("Hiroshige", 10)[5]) +
  #scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  coord_flip()+#ylim = c(25, 55)) +
   geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
  #geom_vline(xintercept = 0, linetype = 2) +
  scale_y_continuous(limits = c(-10, 15)) +
  xlab("Difference contrast") +
  ylab("Pecks per min") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        text = element_text(size = 12))


# expectation model

gender_expectation_draws_2 <-
  fitted(peck_model_expectation_gender, 
         newdata = new_data_gender_2, summary = F, re_formula = NA) %>% 
  as_tibble() %>% 
  rename(Hungry_Women = V1, Hungry_Men = V2, Satiated_Women = V3, Satiated_Men = V4) %>% 
  bind_cols(baseline_peck_predictions %>% select(Baseline_estimate)) %>% 
  pivot_longer(names_to = "Group", values_to = "Posterior_estimate", cols = 1:4) %>% 
  separate(sep = "_", col = Group, into = c("Treatment", "Gender"))
  
calculate_all_the_diffs_4 <-
  fitted(peck_model_expectation_gender, 
         newdata = new_data_gender_2, summary = F, re_formula = NA) %>% 
  as_tibble() %>% 
  rename(Hungry_Women = V1, Hungry_Men = V2, Satiated_Women = V3, Satiated_Men = V4) %>% 
  mutate(Women_h_s_diff = Hungry_Women - Satiated_Women,
         Men_h_s_diff = Hungry_Men - Satiated_Men,
         diff_diff = Women_h_s_diff - Men_h_s_diff) %>% 
  select(contains("diff")) %>%
  rename(`H-S (women)` = Women_h_s_diff,
         `H-S (men)` = Men_h_s_diff,
         `Interaction` = diff_diff) %>% 
  pivot_longer(cols = 1:3, names_to = "diff_contrast", values_to = "posterior_diff")

gp7 <- 
  gender_expectation_draws_2 %>% 
  ggplot(aes(x = Gender, y = Posterior_estimate)) + 
    stat_slab(alpha = 0.9, shape = 21, aes(fill = Treatment)) +
  stat_slab(aes(y = Baseline_estimate),
              linetype = 2, linewidth = 0.8, slab_fill = "white",
              colour = "black") +
  #stat_halfeye(.width = c(0.66, 0.95), alpha = 0.9,
   #            point_interval = "median_qi", point_fill = "white",
   #            shape = 21, point_size = 4, stroke = 1.5,
   #            fill = met.brewer("Hiroshige", 5)[2]) +
  scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  coord_flip()+#ylim = c(25, 55)) +
  #geom_vline(xintercept = 0, linetype = 2) +
  #scale_y_continuous(breaks = c(, 0, 1)) +
  labs(x = "Gender", y = "Estimated pecks per min", fill = "Indicated\nexpectation") +
  theme_bw() + 
  theme(legend.position = "bottom",
        panel.grid.minor = element_blank(),
        text = element_text(size = 12),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 10))



gp8 <-
  calculate_all_the_diffs_4 %>% 
  ggplot(aes(x = diff_contrast, y = posterior_diff)) + 
    #stat_slab(alpha = 0.9, shape = 21, aes(fill = Gender)) +
  stat_halfeye(.width = c(0.66, 0.95), alpha = 0.9,
               point_interval = "median_qi", point_fill = "white",
               shape = 21, point_size = 3, stroke = 1.5,
               fill = met.brewer("Hiroshige", 10)[5]) +
  #scale_fill_manual(values = met.brewer("Hiroshige", 2)) +
  coord_flip()+#ylim = c(25, 55)) +
   geom_hline(yintercept = 0, linetype = 2, linewidth = 0.75) +
  #geom_vline(xintercept = 0, linetype = 2) +
  scale_y_continuous(limits = c(-10, 15)) +
  xlab("Difference contrast") +
  ylab("Pecks per min") +
  theme_bw() + 
  theme(legend.position = "none",
        panel.grid.minor = element_blank(),
        text = element_text(size = 12))
```


$~$

### Find the difference between baseline and observer estimates

**Table S5**. The degree to which each group of observer's overestimates feeding rate (number of ground pecks per minute)

```{r}
baseline_peck_predictions %>% select(Baseline_estimate) %>%  bind_cols(
  
  as_draws_df(peck_model_treatment) %>% 
    mutate(Hungry = exp(b_Bias_treatmentHungry),
           Satiated = exp(b_Bias_treatmentSatiated)) %>% 
    select(Hungry, Satiated)) %>% 
  mutate(`Bias treatment Satiated / Baseline` = Satiated / Baseline_estimate,
         `Bias treatment Hungry / Baseline` = Hungry / Baseline_estimate) %>% 
  select(contains("Bias")) %>% 
  pivot_longer(cols = everything(), values_to = "estimate", names_to = "Stat") %>% 
  group_by(Stat) %>% 
  summarise_draws("median", "sd", ~quantile(.x, probs = c(0.025, 0.975), na.rm = TRUE), .cores = 4) %>% 
  select(-variable) %>% 
  
  bind_rows(
    
    baseline_peck_predictions %>% select(Baseline_estimate) %>%  bind_cols(
      
      as_draws_df(peck_model_expectation) %>% 
        mutate(Hungry = exp(b_ExpectationHungry),
               Satiated = exp(b_ExpectationSatiated)) %>% 
        select(Hungry, Satiated)) %>% 
      mutate(`Expectation Satiated / Baseline` = Satiated / Baseline_estimate,
             `Expectation Hungry / Baseline` = Hungry / Baseline_estimate) %>% 
      select(contains("Expectation")) %>% 
      pivot_longer(cols = everything(), values_to = "estimate", names_to = "Stat") %>% 
      group_by(Stat) %>% 
      summarise_draws("median", "sd", ~quantile(.x, probs = c(0.025, 0.975), na.rm = TRUE), .cores = 4) %>% 
      select(-variable)
  ) %>% 
  pander()
```


# Figure 1

Option 1

```{r, fig.height=12, width = 10}
(p2 + p3) / (p4 + p5) / (p6 + p7) / (p8 + p9) +
   plot_annotation(tag_levels = 'a')
```

```{r, fig.height=12, width = 10, include=FALSE}
(gp1 + gp2) / (gp3 + gp4) / (gp5 + gp6) / (gp7 + gp8) +
   plot_annotation(tag_levels = 'a')
```

**Figure 1**. Posterior mean estimates and difference contrasts for observer estimated group foraging percentage and individual feeding rates. The coloured area is the posterior distribution and the white point is the mean estimate with associated 67% and 95% credible intervals. The distributions shown with dashed lines are the posterior for baseline peck rate, estimated from a random sample of pigeons appearing in the video.


# Session info

```{r}
sessionInfo() %>% pander
```

